← Search

Kilian Kleeberger

7 accepted papers

2023

Towards Packaging Unit Detection for Automated Palletizing Tasks

IROS 2023poster

For various automated palletizing tasks, the detection of packaging units is a crucial step preceding the actual handling of the packaging units by an industrial robot. We propose an approach to this challenging problem that is fully trained on synthetically generated data and can be robustly applie…

Cited by 0SourceScholar
2021

Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation

ICRA 2021poster

Single shot approaches have demonstrated tremendous success on various computer vision tasks. Finding good parameterizations for 6D object pose estimation remains an open challenge. In this work, we propose different novel parameterizations for the output of the neural network for single shot 6D obj…

Cited by 7SourceScholar
2021

Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers

IROS 2021poster

This paper introduces a novel approach for the grasping and precise placement of various known rigid objects using multiple grippers within highly cluttered scenes. Using a single depth image of the scene, our method estimates multiple 6D object poses together with an object class, a pose distance f…

Cited by 8SourceScholar
2021

Real-time Instance Detection with Fast Incremental Learning

ICRA 2021poster

Object instance detection is a highly relevant task to several robotic applications such as automated order picking, or household and hospital assistance robots. In these applications, a holistic scene labeling is often not required whereas it is sufficient to find a certain object type of interest,…

Cited by 7SourceScholar
2020

Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes

IROS 2020poster

In this paper, we introduce a novel learning-based approach for grasping known rigid objects in highly cluttered scenes and precisely placing them based on depth images. Our Placement Quality Network (PQ-Net) estimates the object pose and the quality for each automatically generated grasp pose for m…

Cited by 22SourceScholar
2019

Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking

IROS 2019poster

In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking. The dataset comprises both synthetic and real-world scenes. For both, point clouds, depth images, and annotations comprising the 6D pose (position and orientation), a…

Cited by 94SourceScholar